A road compaction testing device

Abstract

The invention relates to the technical field of road construction material testing, and in particular to a highway compaction detection device. The device determines an initial adjustment coefficient of a highway soil foundation characterizing the deformation sensitivity of the soil foundation surface at a current sampling moment according to the time-series change of edge distribution in an edge image of a highway soil foundation surface during a downward pressure process; then, according to the change of the edge position of the soil foundation surface under stress during the downward pressure process, the unreasonableness of the advancement speed is measured by calculating the advancement speed stability, thereby combining the initial adjustment coefficient of the highway soil foundation and the advancement speed instability to determine the highway soil foundation optimization adjustment coefficient for adjusting the advancement speed at the current sampling moment; finally, the advancement speed is adjusted according to the highway soil foundation optimization adjustment coefficient, thereby obtaining a more reasonable adaptive advancement speed at the current sampling moment, making the real-time downward pressure of the cylinder more reasonable, and making the highway compaction detection result more accurate.

Description

Technical Field

[0001] The invention relates to the technical field of road construction material testing, and in particular to a road compaction detection device. Background Art

[0002] In the process of highway maintenance, when the partially damaged road surface is repaired or re-paved, the compaction degree needs to be measured to ensure that the compaction quality of the newly repaired part meets the requirements. The ring knife method is a destructive detection method, which is suitable for fine-grained soil without aggregate and is relatively simple to operate.

[0003] The patent document with publication number CN212865886U in the prior art discloses a roadbed compaction detection sampling device for highway engineering. The device is a ring cutter installed under a cylinder, and a driving structure is used to drive the ring cutter and the cylinder to move downward. After advancing to a predetermined depth, the soil base is sampled, and then the compaction is detected based on the sample. However, the prior art usually presses the ring cutter downward through the cylinder at a constant speed. When pressing downward at a constant speed, the pressure will continue to change as the pressure is pressed. When the pressure is too high, the soil base sample in the ring cutter will crack and lack blocks, which will lead to inaccurate highway compaction detection results. Summary of the invention

[0004] The present application provides a highway compaction detection device, which first determines an initial adjustment coefficient of a highway subgrade characterizing the deformation sensitivity of the subgrade surface at the current sampling moment according to the time-series changes in the edge distribution in the edge image of the highway subgrade surface during the downward pressing process; then measures the irrationality of the advancement speed by calculating the advancement speed stability according to the edge position changes of the subgrade surface under the action of stress during the downward pressing process, thereby determining an optimization adjustment coefficient of the highway subgrade used to adjust the advancement speed at the current sampling moment in combination with the initial adjustment coefficient of the highway subgrade and the advancement speed instability; finally, the advancement speed is adjusted according to the optimization adjustment coefficient of the highway subgrade, thereby obtaining a more reasonable adaptive advancement speed at the current sampling moment, so that the real-time downward pressure of the cylinder is more reasonable, which solves the problem that when the prior art presses the ring cutter through the cylinder at a constant speed, the pressure will continue to change as the downward pressing proceeds, resulting in cracking and missing blocks of the subgrade sample in the ring cutter due to excessive pressure, and makes the highway compaction detection result more accurate.

[0005] The present application provides a road compaction detection device, including a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement the following monitoring process:

[0006] Obtaining at least two highway subgrade surface images at different viewing angles at each sampling moment, and obtaining a highway subgrade surface edge image at each sampling moment based on all highway subgrade surface images at each sampling moment;

[0007] Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method;

[0008] Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment;

[0009] According to the fluctuation of the position of edge pixels in each image block in the edge difference image of the highway subgrade surface at each sampling time before the current sampling time, the instability of the advancement speed corresponding to the highway compaction detection at the current sampling time is determined;

[0010] According to the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed, determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment; according to the highway soil foundation optimization adjustment coefficient, adjust the advancing speed at the current sampling moment to obtain the adaptive advancing speed at the current sampling moment;

[0011] The highway compaction degree is detected after the highway subgrade is advanced according to the adaptive advancement speed at the current sampling moment.

[0012] Furthermore, the process of acquiring the highway subgrade surface edge difference image includes:

[0013] The difference image between the highway subgrade surface edge image at each sampling moment and the highway subgrade surface edge image at the previous sampling moment is used as the highway subgrade surface edge difference image at each sampling moment.

[0014] Furthermore, the process of obtaining the initial adjustment coefficient of the highway subgrade includes:

[0015] According to the relative length of the longest edge line of each image block in the highway subgrade surface edge difference image at each sampling moment, the corresponding local deformation degree is determined;

[0016] Determine the deformation sensitivity of each image block in the highway subgrade surface edge difference image at each sampling moment according to the average value of the local deformation degree of each image block in all highway subgrade surface edge images before each sampling moment;

[0017] According to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment, the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment is determined.

[0018] Furthermore, the process of obtaining the local deformation degree includes:

[0019] Each image block in the highway subgrade surface edge difference image at each sampling moment is sequentially used as a target image block;

[0020] When there is no edge line other than a single edge point in the target image block, the local deformation degree of the target image block is set to a preset deformation degree value;

[0021] When there are edge lines other than single edge points in the target image block, the average of the lengths of all edge lines corresponding to the non-single edge points in the target image block is used as the reference edge line length; and the local deformation degree of the target image block is determined according to the ratio between the longest edge line length in the target image block and the reference edge line length.

[0022] Furthermore, the method of determining the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment includes:

[0023] The normalized value of the accumulated values ​​of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment is used as the corresponding highway subgrade initial adjustment coefficient.

[0024] Furthermore, the process of obtaining the propulsion speed instability includes:

[0025] In each image block of the highway subgrade surface edge difference image at each sampling time, an image coordinate system is constructed with the centroid point of the image block as the coordinate origin, and the vector corresponding to each edge point pointed to by the coordinate origin is used as the edge vector of each edge point in the image block; the edge vectors of all edge points in the image block are added to determine the edge distance vector of the corresponding image block; the modulus of the edge distance vector of the image block is positively correlated to determine the deformation centripetal density of each image block at each sampling time;

[0026] The deformation centripetal density variance of each image block at all sampling moments before the current sampling moment is calculated; the advancement speed instability corresponding to the road compaction detection at the current moment is determined according to the mean of the deformation centripetal density variance corresponding to all image blocks.

[0027] Furthermore, the process of obtaining the highway subgrade optimization adjustment coefficient includes:

[0028] The product of the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed is normalized to determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment.

[0029] Furthermore, the process of acquiring the adaptive propulsion speed includes:

[0030] The negative correlation mapping value of the highway soil foundation optimization adjustment coefficient is used as a weight to weight the advancing speed at the current sampling moment to determine the adaptive advancing speed at the current sampling moment.

[0031] Further, the road compaction detection after the road subgrade advancement according to the adaptive advancement speed at the current sampling moment includes:

[0032] Continue to advance downward at an adaptive advancing speed until the ring cutter and digging teeth of the highway compaction detection equipment reach the preset advancing depth and then conduct highway compaction detection.

[0033] A highway compaction detection device comprises a ring cutter and a cylinder body, the highway compaction detection device further comprises a digging tooth advancing slide rail 1, a digging tooth advancing slide rail 2, a digging tooth 1, a digging tooth 2 and a highway soil foundation advancing monitoring device, the digging tooth advancing slide rail 1 and the digging tooth advancing slide rail 2 are respectively mounted on both sides of the cylinder body, the digging tooth 1 is fixedly mounted inside the digging tooth advancing slide rail 1, and the digging tooth 2 is fixedly mounted inside the digging tooth advancing slide rail 2, the highway soil foundation advancing monitoring device comprises an image collector 1, an image collector 2 and a controller, the image collector 1 and the image collector 2 are both mounted on the outside of the cylinder body, the controller is mounted inside the cylinder body, the image collector 1 and the image collector 2 are both connected to the controller by signal, the image collector 1 and the image collector 2 are used to obtain highway soil foundation surface images of at least two different perspectives, and the controller obtains the highway soil foundation surface edge image at each sampling moment according to all highway soil foundation surface images at each sampling moment;

[0034] Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method;

[0035] Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method;

[0036] Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment;

[0037] According to the fluctuation of the position of edge pixels in each image block in the edge difference image of the highway subgrade surface at each sampling time before the current sampling time, the instability of the advancement speed corresponding to the highway compaction detection at the current sampling time is determined;

[0038] According to the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed, determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment; according to the highway soil foundation optimization adjustment coefficient, adjust the advancing speed at the current sampling moment to obtain the adaptive advancing speed at the current sampling moment;

[0039] The highway compaction degree is detected after the highway subgrade is advanced according to the adaptive advancement speed at the current sampling moment.

[0040] This application has the following beneficial effects:

[0041] First of all, it is necessary to consider that the different components of the soil base, such as water content and particle size, have different abilities to resist deformation when subjected to the same pressure. The highway soil base with greater resistance to deformation usually has a smaller deformation amplitude when subjected to a larger dragging pressure and the stress between the ring cutter, digging teeth and soil base during the advancement process. In other words, the soil base sample in the corresponding ring cutter is less likely to crack or lack blocks, and the corresponding advancement speed does not need to be adjusted. Therefore, according to the time series changes in the edge distribution in the edge image of the highway soil base surface during the downward pressure process, the initial adjustment coefficient of the highway soil base that characterizes the deformation sensitivity of the soil base surface at the current sampling time is determined.

[0042] During the advancement process, the stress between the cutter ring, digging teeth and soil foundation will continue to increase, and the edge points of the image block will diffuse away from the cutter ring and digging teeth as the stress increases. At the corresponding advancement speed, the slower the diffusion speed, the more stable the advancement process, the better the corresponding advancement rate, and the less need to adjust the advancement speed. Therefore, according to the change of the edge position of the soil foundation surface under the stress during the downward pressure process, the unreasonableness of the advancement speed is measured by calculating the stability of the advancement speed.

[0043] Finally, the highway subgrade initial adjustment coefficient and the advancement speed stability are combined to determine the highway subgrade optimization adjustment coefficient used to adjust the advancement speed at the current sampling time, so that the adaptive advancement speed adjusted according to the highway subgrade optimization adjustment coefficient is more in line with the subgrade sampling conditions, and the real-time downward pressure of the cylinder is more reasonable, thereby improving the accuracy of the highway compaction test results. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A structural front view of a road compaction detection device provided by one embodiment of the present invention;

[0046] Figure 2 A schematic top view of a ring cutter and a tooth portion of a road compaction detection device provided by an embodiment of the present invention;

[0047] Figure 3 A schematic flow chart of a data processing process in a road compaction detection device provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of an image of a highway subgrade surface edge provided by an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of a highway subgrade surface edge difference image provided by an embodiment of the present invention;

[0050] Figure 1 The winning numbers are: 1. Ring cutter; 2. Cylinder; 3. Digging tooth pushing slide rail 1; 4. Image collector 1; 5. Digging tooth 1; 6. Digging tooth pushing slide rail 2; 7. Image collector 2; 8. Digging tooth 2. DETAILED DESCRIPTION

[0051] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation method, structure, characteristics and effects of a highway compaction detection device proposed according to the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment, and the specific features, structures or characteristics in one or more embodiments may be combined in any suitable form. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0053] The specific scheme of a road compaction detection device provided by the present invention is described in detail below with reference to the accompanying drawings.

[0054] A road compaction detection device embodiment 1:

[0055] This embodiment provides a road compaction detection device, including a ring cutter and a cylinder, the ring cutter and the cylinder are conventional structures in a roadbed compaction detection sampling device for highway engineering disclosed in the patent document with publication number CN212865886U, and the ring cutter is installed under the cylinder; the road compaction detection device also includes a tooth push rail, a tooth and a road soil foundation push monitoring device. Figure 1 , which shows a structural front view of a highway compaction detection device provided by an embodiment of the present invention, wherein the highway compaction detection device body comprises: a ring cutter 1, a cylinder 2, a digging tooth advancing slide rail 1 3, an image collector 1 4, a digging tooth 1 5, a digging tooth advancing slide rail 2 6, an image collector 2 7 and a digging tooth 2 8; wherein the ring cutter 1 is installed directly below the cylinder 2, the digging tooth advancing slide rail 1 3 and the digging tooth advancing slide rail 2 6 are respectively installed on both sides of the cylinder 2, and the corresponding digging tooth 1 5 is fixedly installed inside the digging tooth advancing slide rail 1 3, and the digging tooth 2 8 is fixedly installed inside the digging tooth advancing slide rail 2 6; please refer to Figure 2 , which shows a schematic top view of a ring cutter and a tooth portion of a road compaction detection device provided by an embodiment of the present invention, Figure 2 In the figure, the digging tooth 1 5 and the digging tooth 2 8 are located on both sides of the ring cutter 1 in relative position. After the advancement process is completed, the digging tooth 1 5 and the digging tooth 2 8 are further pushed by adjusting the digging tooth advancement slide rail 1 3 and the digging tooth advancement slide rail 2 6, so that the road surface sampling sample in the ring cutter 1 is completely extracted.

[0056] The image collector 1 4 and the image collector 2 7 are both connected to the internal controller of the highway compaction detection equipment, and together constitute the highway soil subgrade advancement monitoring equipment; the image collector 1 4 and the image collector 2 7 are used to photograph the surface images of the highway soil subgrade of the ring cutter 1 and the cylinder 2 during the advancement process, the digging teeth 1 5, the digging teeth 2 8, and the highway road surface between the ring cutter 1 and the cylinder 2 at each sampling time. It should be noted that all the digging teeth involved in the subsequent descriptions represent the digging teeth 1 5 and the digging teeth 2 8 at the same time, and no further description is made here.

[0057] The controller can be a data processing chip such as a CPU, MCU, or a data processing device such as a computer host. The image collector signal is connected to the controller, and the two can be connected by wire through a data transmission line, and the output end of the image collector is connected to the input end of the controller, or wireless connection can be achieved through wireless communication methods such as Bluetooth and WiFi.

[0058] In addition, the image collector and the controller can also be integrated to form a device that integrates image collection and image processing functions. At each sampling time, the first highway subgrade surface image is collected by the image collector 14, and the second highway subgrade surface image is collected by the image collector 27. The first highway subgrade surface image obtained by the image collector 14 and the second highway subgrade surface image obtained by the image collector 27 at each sampling time are further transmitted to the internal controller of the highway compaction detection device, and the highway compaction detection is performed according to the predetermined data processing process with the help of the controller. Please refer to Figure 3 , which shows a flow chart of a data processing process in a road compaction detection device provided by an embodiment of the present invention, comprising the following steps:

[0059] Step S301: obtaining at least two highway subgrade surface images of different viewing angles at each sampling moment, and obtaining a highway subgrade surface edge image at each sampling moment based on all highway subgrade surface images at each sampling moment.

[0060] Preferably, in some possible implementations of the embodiments of the present invention, the highway subgrade surface edge image at each sampling moment is acquired according to the first highway subgrade surface image and the second highway subgrade surface image at each sampling moment by an internal controller of the highway compaction detection equipment.

[0061] In a specific implementation of the embodiment of the present invention, the method for obtaining the edge image of the road subgrade surface includes: at each sampling time, using an affine transformation matrix to perform image fusion based on the first road subgrade surface image and the second road subgrade surface image, and determining the fused image of the road subgrade surface at each sampling time; graying the fused image of the road subgrade surface and performing edge detection using the canny edge detection algorithm to determine the edge image of the road subgrade surface at each sampling time. Figure 4 , which shows a schematic diagram of a highway subgrade surface edge image provided by an embodiment of the present invention, wherein the white lines are edge lines in the highway subgrade surface edge image. In a specific implementation of an embodiment of the present invention, the sampling frequency is set to collect once every 2 seconds. It should be noted that the affine transformation matrix, grayscale and canny edge detection algorithm are all technical means well known to those skilled in the art, and will not be further elaborated here.

[0062] Step S302: determining a highway subgrade surface edge difference image at each sampling moment according to the edge distribution difference of the highway subgrade surface edge images between adjacent sampling moments; and dividing each highway subgrade surface edge image into at least two image blocks in the same division manner.

[0063] The highway subgrade surface edge difference image can show the difference in edge distribution between the highway subgrade surface edge images that are adjacent in time sequence, so that the change of the edge in time sequence can be more intuitively represented according to the highway subgrade surface edge difference image, making the subsequent analysis more intuitive. The purpose of dividing the image blocks is to combine the local information for analysis, so as to more accurately analyze the overall details of the highway subgrade surface edge difference image, and to a certain extent, it can improve the analysis efficiency.

[0064] Preferably, in some possible implementations of the embodiments of the present invention, the process of acquiring the edge difference image of the highway subgrade surface includes:

[0065] The difference image between the highway subgrade surface edge image at each sampling moment and the highway subgrade surface edge image at the previous sampling moment is taken as the highway subgrade surface edge difference image at each sampling moment; that is, the edges that appear in the highway subgrade surface edge image at each sampling moment but do not appear in the highway subgrade surface edge image at the previous sampling moment are retained, so as to obtain the highway subgrade surface edge difference image at each sampling moment. The edges in the highway subgrade surface edge difference image represent the edge changes of the highway subgrade surface edge difference image at each sampling moment compared with the previous sampling moment, and indirectly represent the influence of the highway subgrade on the advancement process at each sampling moment. The more obvious the edge features in the highway subgrade surface edge difference image, the greater the influence of the advancement process. Please refer to Figure 5 , which shows a schematic diagram of a highway subgrade surface edge difference image provided by an embodiment of the present invention, wherein the white lines are edge lines in the highway subgrade surface edge difference image.

[0066] Preferably, in some possible implementations of the embodiments of the present invention, the process of dividing each highway subgrade surface edge image into at least two image blocks in the same division manner includes:

[0067] The highway subgrade surface edge image is divided into 36 rectangular image blocks of the same shape and size in the form of 6×6, that is, the highway subgrade surface edge image is divided into six rows and six columns by analogy with a matrix, and each matrix position corresponds to an image block. In other specific implementations of the embodiments of the present invention, image block division can also be performed by other image block division numbers or other image block division methods, which will not be further elaborated here.

[0068] Step S303: determining the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment.

[0069] When the soil base components, such as water content and particle size, are different, the ability to resist deformation under the same pressure is different; the road soil base with greater anti-deformation ability usually has a smaller deformation amplitude when subjected to a larger dragging pressure and the stress between the ring cutter, digging teeth and soil base during the advancement process, that is, the less likely the soil base sample in the corresponding ring cutter is to crack or lack of blocks, the less the corresponding advancement speed needs to be adjusted; therefore, according to the time series change of the edge distribution in the edge image of the road soil base surface during the downward pressure process, the initial adjustment coefficient of the road soil base that characterizes the deformation sensitivity of the soil base surface at the current sampling time is determined. The larger the initial adjustment coefficient of the road soil base, the more obvious the deformation of the soil base surface during the downward pressure process, that is, the worse the anti-deformation ability, and the more it is necessary to adjust the advancement speed to avoid the problem of cracking and missing blocks of the soil base sample in the ring cutter.

[0070] Preferably, in a specific implementation of the embodiment of the present invention, the process of obtaining the initial adjustment coefficient of the highway subgrade includes:

[0071] According to the relative length of the longest edge line of each image block in the highway subgrade surface edge difference image at each sampling moment, the corresponding local deformation degree is determined. Preferably, in a specific implementation of the embodiment of the present invention, the process of obtaining the local deformation degree includes:

[0072] Each image block in the edge difference image of the highway subgrade surface at each sampling moment is taken as the target image block in turn; when there is no edge line other than a single edge point in the target image block, the local deformation degree of the target image block is set to a preset deformation degree value; when there is an edge line other than a single edge point in the target image block, the average of all edge line lengths corresponding to the non-single edge point in the target image block is taken as the reference edge line length; the local deformation degree of the target image block is determined according to the ratio between the longest edge line length in the target image block and the reference edge line length. Under the action of stress, the deformation of the highway subgrade surface is usually a continuous long edge. The longer the corresponding edge in each image block of the edge difference image of the highway subgrade surface, the greater the influence of stress, and the greater the corresponding local deformation degree. In obtaining the local deformation degree, the role of the reference edge line length is to reduce the dimensional influence and improve the robustness of the calculated local deformation degree. The implementer can also use the longest edge line length as the local deformation degree according to the specific implementation environment, which will not be further elaborated here.

[0073] In a specific implementation of the embodiment of the present invention, when the target image block When there are edge lines other than single edge points in , the corresponding local deformation degree acquisition process is expressed as follows: ;in, The target image block The local deformation degree; The target image block The longest edge line length in ; The target image block The mean of all edge line lengths corresponding to the non-single edge points in , that is, the reference edge line length.

[0074] In a specific implementation of an embodiment of the present invention, the preset deformation degree value is set to 0. Because when there is no edge line other than a single edge point in the target image block, it indicates that the stress is minimally affected or not affected at all. The implementer can adjust the preset deformation degree value according to the specific implementation environment.

[0075] Further, according to the average of the local deformation of each image block in all highway subgrade surface edge images before each sampling time, the deformation sensitivity of each image block in the highway subgrade surface edge difference image at each sampling time is determined. The larger the corresponding deformation sensitivity, the larger the overall local deformation of each image block in all highway subgrade surface edge difference images before each sampling time, and the greater the overall stress influence.

[0076] Finally, according to the overall deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment, the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment is determined; since the greater the deformation sensitivity of the image block, the greater the influence of stress on the corresponding image block; therefore, the greater the overall deformation sensitivity of all image blocks, the greater the initial adjustment coefficient of the highway subgrade corresponding to the surface deformation sensitivity, and the more it is necessary to adjust the advancement speed at the current moment.

[0077] In a specific implementation method of an embodiment of the present invention, the normalized value of the accumulated values ​​of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment is used as the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment; the implementer may also replace the accumulated value with the mean value of the deformation sensitivity of all image blocks to calculate the initial adjustment coefficient of the highway subgrade, which will not be further elaborated here.

[0078] In a specific implementation of the embodiment of the present invention, the method for obtaining the initial adjustment coefficient of the highway subgrade is expressed in the formula as follows: ;in, The current sampling time The initial adjustment coefficient of the highway soil foundation corresponding to the compaction test of the lower highway; is the number of image blocks in the highway subgrade surface edge difference image; The current sampling time The number of previous sampling moments; The current sampling time Before The first The local deformation of an image block; The current sampling time The first The deformation sensitivity of an image patch. It is a normalization function. The implementer can adjust the normalization method according to the specific implementation environment.

[0079] The larger the initial adjustment coefficient of the highway subgrade, the greater the impact of stress on the highway subgrade surface at the current moment, the worse the corresponding subgrade deformation resistance, and the more likely the subgrade sample in the ring cutter will crack and miss blocks, that is, the more unreasonable the current advancement speed is, and the more adjustment is needed.

[0080] Step S304: Determine the advancement speed instability corresponding to the highway compaction detection at the current sampling moment according to the fluctuation of the position of edge pixels in each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment.

[0081] Step S303 analyzes the necessity of adjusting the advancement speed based on the deformation resistance of the soil base. Further, in step S304, the advancement speed is further adjusted based on the characteristics that the stress between the ring cutter, the digging teeth and the soil base will continue to increase during the advancement process, and the edge points of the image block will diffuse in the direction away from the ring cutter and the digging teeth as the stress increases, so that the adaptive advancement speed obtained after adjustment is more accurate. At a certain advancement speed, the slower the diffusion speed of the edge points of the image block, the more stable the advancement process, the better the corresponding advancement rate, and the less need to adjust the advancement speed. Therefore, according to the change in the edge position of the soil base surface under the action of stress during the downward pressure process, the unreasonableness of the advancement speed is measured by calculating the stability of the advancement speed.

[0082] In some possible implementations of the embodiments of the present invention, the process of obtaining propulsion speed instability includes:

[0083] In each image block of the edge difference image of the highway subgrade surface at each sampling moment, an image coordinate system is constructed with the centroid point of the image block as the coordinate origin, and the vector corresponding to each edge point pointed by the coordinate origin is used as the edge vector of each edge point in the image block; the edge vectors of all edge points in the image block are added to determine the edge distance vector of the corresponding image block; the modulus of the edge distance vector of the image block is positively correlated to determine the deformation centripetal density of each image block at each sampling moment; the variance of the deformation centripetal density of each image block at all sampling moments before the current sampling moment is calculated; and the advancement speed instability corresponding to the highway compaction detection at the current moment is determined according to the mean of the variance of the deformation centripetal density corresponding to all image blocks.

[0084] The highway subgrade surface edge difference image can intuitively reflect the edge changes corresponding to each sampling moment compared with the previous sampling moment; according to the characteristic that the edge points of the image block will diffuse in the direction away from the ring cutter and the digging teeth as the stress increases, each edge point in the highway subgrade surface edge image at each sampling moment is usually obtained by displacing each edge point in the highway subgrade surface edge image at the previous sampling moment in the direction away from the ring cutter and the digging teeth. Therefore, the edge points in the highway subgrade surface edge difference image at adjacent sampling moments usually show a trend of displacement changes. The larger the corresponding displacement change, the faster the deformation rate of the subgrade surface. Therefore, the deformation centripetal density obtained by accumulating the vectors from the image block centroid to each edge point in each image block is used to characterize the edge points of each image block. The position state of the highway subgrade surface edge difference image at adjacent sampling moments usually shows the characteristics of trend displacement change. The greater the difference in deformation centripetal density at adjacent sampling moments, the greater the intensity or speed of the corresponding trend displacement change, that is, the faster the deformation rate of the subgrade surface; and the deformation centripetal density variance represents the overall fluctuation of the deformation centripetal density. The larger the variance of the deformation centripetal density of the image block, the faster the deformation rate of the subgrade surface corresponding to the image block, and the more unreasonable the corresponding advancement speed; further, by representing the advancement speed instability of the mean of the deformation centripetal density variance corresponding to all image blocks, the overall size of the subgrade deformation rate at the current moment is represented as a whole, thereby indirectly representing the irrationality of the advancement speed, that is, the degree of necessity for adjustment.

[0085] In a specific implementation of the embodiment of the present invention, the process of obtaining the deformation centripetal density is expressed by the formula: ;in, For the The image block is The centripetal density of deformation at each sampling moment; For the The image block is The edge distance vector at each sampling moment; It is the diagonal length of each image block in the edge difference image of the highway subgrade surface, which is used to reduce the influence of the image block size on the calculation of the deformation centripetal density, so that the calculated deformation centripetal density is more robust. The implementer can also replace the diagonal length with the area and perimeter of the image block to calculate the deformation centripetal density, which will not be further elaborated here.

[0086] In a specific implementation of the embodiment of the present invention, the process of obtaining the propulsion speed instability is expressed by the formula: ;in, The current sampling time The instability of the advancing speed corresponding to the compaction test of the lower highway; is the number of image blocks in the highway subgrade surface edge difference image; The current sampling time All previous sampling times The variance of the deformation centripetal density of an image patch.

[0087] In other possible implementation methods of the embodiments of the present invention, the absolute value of the difference between the deformation centripetal density of each image block at each sampling moment and the deformation centripetal density at the previous sampling moment is used as the deformation centripetal density change value of each image block at each sampling moment; the average of the deformation centripetal density change values ​​of each image block at all sampling moments before the current sampling moment is used as the reference change value of each image block; and then the average of the reference change values ​​of all image blocks at the current sampling moment is used as the corresponding advancement speed instability; that is, the advancement speed instability is characterized by the overall change of the deformation centripetal density in time series, which will not be further elaborated here.

[0088] Step S305: Determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment according to the highway soil foundation initial adjustment coefficient and the advancement speed instability; adjust the advancement speed at the current sampling moment according to the highway soil foundation optimization adjustment coefficient to obtain the adaptive advancement speed at the current sampling moment.

[0089] Since the initial adjustment coefficient of the highway subgrade and the instability of the advancement speed can both characterize the degree of adjustment of the advancement speed to a certain extent, the initial adjustment coefficient of the highway subgrade and the instability of the advancement speed are combined, and the advancement speed at the current sampling moment is adjusted according to the combined optimized adjustment coefficient of the highway subgrade, so as to obtain a more accurate adaptive advancement speed at the current moment.

[0090] Preferably, in some possible implementations of the embodiments of the present invention, the process of obtaining the highway subgrade optimization adjustment coefficient includes:

[0091] The larger the initial adjustment coefficient of the highway subgrade, the greater the instability of the advancement speed, and the more it is necessary to adjust the advancement speed; therefore, the initial adjustment coefficient of the highway subgrade and the instability of the advancement speed are positively correlated with the highway subgrade optimization adjustment coefficient that characterizes the degree of advancement speed adjustment; therefore, the product of the initial adjustment coefficient of the highway subgrade and the instability of the advancement speed is normalized to determine the highway subgrade optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling time.

[0092] In a specific implementation of the embodiment of the present invention, the method for obtaining the highway subgrade optimization adjustment coefficient is expressed by the formula: ;in, The current sampling time The optimization adjustment coefficient of the highway soil foundation corresponding to the compaction test of the lower highway; The current sampling time The initial adjustment coefficient of the highway soil foundation corresponding to the compaction test of the lower highway; The current sampling time The instability of the advancing speed corresponding to the compaction test of the lower highway; It is a linear normalization function. The implementer can adjust the normalization method according to the specific implementation environment.

[0093] Preferably, in some possible implementations of the embodiments of the present invention, the process of acquiring the adaptive propulsion speed includes:

[0094] It should be considered that when the advancement speed is too large, the initial adjustment coefficient of the highway subgrade is usually large under the influence of stress, and the corresponding advancement speed instability in the dimension of edge diffusion is usually large, and the advancement speed instability and the initial adjustment coefficient of the highway subgrade are both positively correlated with the highway subgrade optimization adjustment coefficient. Therefore, the corresponding highway subgrade optimization adjustment coefficient should adjust the advancement speed downward. Therefore, the negative correlation mapping value of the highway subgrade optimization adjustment coefficient is used as the weight to weight the advancement speed at the current sampling time to determine the adaptive advancement speed at the current sampling time.

[0095] In a specific implementation of the embodiment of the present invention, the method for obtaining the adaptive propulsion speed is expressed in the formula as follows: ;in, The current sampling time The adaptive propulsion speed under the above condition is the propulsion speed after adjustment; The current sampling time The advancing speed under adjustment is the advancing speed before adjustment; The current sampling time The road subgrade optimization adjustment coefficient corresponding to the road compaction detection is obtained. In a specific implementation of the embodiment of the present invention, the current sampling time The advancement speed before adjustment, that is, the initial speed value, is set to 10 mm per minute. The implementer can adjust it according to the specific implementation environment, and no further elaboration is made here.

[0096] Step S306: After the highway soil base is advanced according to the adaptive advancement speed at the current sampling time, the highway compaction degree is detected.

[0097] The purpose of this application is to detect the compactness of the highway. The purpose of adjusting the advancement speed is to ensure that the collected soil base samples are not damaged or the degree of damage is reduced, so that the highway compactness detection is more accurate. Therefore, it is further necessary to perform the highway compactness detection after the highway soil base is advanced according to the adaptive advancement speed.

[0098] Preferably, in some possible implementations of the embodiments of the present invention, performing road compaction detection after performing road subgrade advancement according to the adaptive advancement speed at the current sampling moment includes:

[0099] Continue to push downward at an adaptive pushing speed until the ring cutter and the digging teeth of the road compaction detection equipment reach the preset pushing depth, and then perform the road compaction detection. In a specific implementation of the embodiment of the present invention, the preset pushing depth is set to 60mm, and the implementer can adjust it according to the specific implementation environment, which will not be further described here.

[0100] Preferably, in other possible implementations of the embodiments of the present invention, the adaptive adjustment of the advancement speed is performed in an iterative manner until the ring cutter and the digging teeth of the highway compaction detection device reach the preset advancement depth and then the highway compaction detection is performed. Specifically, after calculating the adaptive advancement speed at the current moment, the preset number of sampling moments are advanced in real time at the adaptive advancement speed, and the real-time highway subgrade optimization adjustment coefficient after the real-time advancement is calculated according to the highway subgrade surface edge difference image at the preset number of sampling moments, and the adaptive advancement speed calculated before the preset number of sampling moments is adjusted by the highway subgrade optimization adjustment coefficient, and the advancement is continued at the adjusted speed; and after 10 sampling moments, the real-time highway subgrade optimization adjustment coefficient after the real-time advancement is calculated again, and the corresponding advancement speed is adjusted again, and the iteration is continued until the ring cutter and the digging teeth of the highway compaction detection device reach the preset advancement depth. Because the stress between the ring cutter, the digging teeth and the subgrade will continue to increase with the advancement of the highway compaction detection device, the real-time adjustment of the advancement speed can better protect the subgrade sample, so that the accuracy of the highway compaction detection result is higher.

[0101] Preferably, in some possible implementations of the embodiments of the present invention, after the ring cutter and the digging teeth of the highway compaction detection equipment reach a preset advancement depth, the digging teeth 1 5 and the digging teeth 2 8 are further pushed by adjusting the digging teeth advancement slide rail 1 3 and the digging teeth advancement slide rail 2 6, so that the road surface sampling sample in the ring cutter 1 is completely extracted, that is, the soil sample around the ring cutter 1 is flattened with a soil trimming knife, and the ring cutter 1 is taken out, and care is taken not to disturb the soil sample. After the road surface sampling sample is extracted, the compaction test is performed, and the specific process includes:

[0102] (1) Weigh the total mass of the cutter ring and soil sample: Use a balance to accurately weigh the total mass of the cutter ring and the soil sample.

[0103] (2) Measure the volume of the cutter ring: Determine the volume of the cutter ring according to its specifications.

[0104] (3) Calculation of wet density: wet density is equal to the total mass of the cutter ring and soil sample minus the mass of the cutter ring divided by the volume of the cutter ring.

[0105] (4) Obtaining the moisture content of the soil sample: Determine the moisture content of the soil sample by drying method, etc.

[0106] (5) Calculation of compaction degree: Based on the wet density, moisture content and maximum dry density of the soil (determined by a standard compaction test), calculate the compaction degree using the corresponding formula.

[0107] It should be noted that compaction detection is an existing technology well known to those skilled in the art, and the significance of the compaction calculation process will not be further elaborated here.

[0108] A road compaction detection device embodiment 2:

[0109] This embodiment provides a road compaction detection device, including a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement the following monitoring process:

[0110] Obtaining at least two highway subgrade surface images at different viewing angles at each sampling moment, and obtaining a highway subgrade surface edge image at each sampling moment based on all highway subgrade surface images at each sampling moment;

[0111] Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method;

[0112] Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment;

[0113] According to the fluctuation of the position of edge pixels in each image block in the edge difference image of the highway subgrade surface at each sampling time before the current sampling time, the instability of the advancement speed corresponding to the highway compaction detection at the current sampling time is determined;

[0114] According to the initial adjustment coefficient of the highway soil foundation and the instability of the advancement speed, the optimization adjustment coefficient of the highway soil foundation corresponding to the highway compaction detection at the current sampling time is determined; the advancement speed at the current sampling time is adjusted according to the optimization adjustment coefficient of the highway soil foundation to obtain the adaptive advancement speed at the current sampling time;

[0115] The highway compaction degree is detected after the highway subgrade is advanced according to the adaptive advancement speed at the current sampling moment.

[0116] Therefore, the highway compaction detection device provided in this embodiment is essentially a processor device, which is implemented by an internal data processing process. Since the data processing process has been described in detail in the above-mentioned embodiment 1 of the highway compaction detection device, it will not be repeated here.

[0117] In summary, a highway compaction detection device determines the initial adjustment coefficient of the highway subgrade that characterizes the deformation sensitivity of the subgrade surface at the current sampling moment according to the time series change of the edge distribution in the edge image of the highway subgrade surface during the downward pressure process; then, according to the change of the edge position of the subgrade surface under the stress during the downward pressure process, the unreasonableness of the advancement speed is measured by calculating the advancement speed stability, so as to determine the highway subgrade optimization adjustment coefficient for adjusting the advancement speed at the current sampling moment in combination with the initial adjustment coefficient of the highway subgrade and the advancement speed instability, and finally adjust the advancement speed according to the highway subgrade optimization adjustment coefficient, so as to obtain a more reasonable adaptive advancement speed at the current sampling moment, so that the real-time downward pressure of the cylinder is more reasonable, which solves the problem that when the existing technology presses the ring cutter through the cylinder at a constant speed, the pressure will change continuously as the downward pressure proceeds, resulting in cracking and missing blocks of the subgrade sample in the ring cutter due to excessive pressure, so that the highway compaction detection result is more accurate.

[0118] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A road compaction detection device, characterized in that: The system comprises a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement the following monitoring process: Obtaining at least two highway subgrade surface images at different viewing angles at each sampling moment, and obtaining a highway subgrade surface edge image at each sampling moment based on all highway subgrade surface images at each sampling moment; Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method; Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment; According to the fluctuation of the position of edge pixels in each image block in the edge difference image of the highway subgrade surface at each sampling time before the current sampling time, the instability of the advancement speed corresponding to the highway compaction detection at the current sampling time is determined; According to the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed, determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment; according to the highway soil foundation optimization adjustment coefficient, adjust the advancing speed at the current sampling moment to obtain the adaptive advancing speed at the current sampling moment; After the highway soil foundation is advanced according to the adaptive advancement speed at the current sampling time, the highway compaction degree is tested; The process of obtaining the initial adjustment coefficient of the highway subgrade includes: According to the relative length of the longest edge line of each image block in the highway subgrade surface edge difference image at each sampling moment, the corresponding local deformation degree is determined; Determine the deformation sensitivity of each image block in the highway subgrade surface edge difference image at each sampling moment according to the average value of the local deformation degree of each image block in all highway subgrade surface edge images before each sampling moment; Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment; The process of obtaining the local deformation degree includes: Each image block in the highway subgrade surface edge difference image at each sampling moment is sequentially used as a target image block; When there is no edge line other than a single edge point in the target image block, the local deformation degree of the target image block is set to a preset deformation degree value; When there are edge lines other than single edge points in the target image block, the average of the lengths of all edge lines corresponding to the non-single edge points in the target image block is used as the reference edge line length; the local deformation degree of the target image block is determined according to the ratio between the longest edge line length in the target image block and the reference edge line length; The method of determining the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment comprises: The normalized value of the accumulated values ​​of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment is used as the corresponding highway subgrade initial adjustment coefficient.

2. A road compaction detection device according to claim 1, characterized in that: The process of acquiring the highway subgrade surface edge difference image includes: The difference image between the highway subgrade surface edge image at each sampling moment and the highway subgrade surface edge image at the previous sampling moment is used as the highway subgrade surface edge difference image at each sampling moment.

3. A road compaction detection device according to claim 1, characterized in that: The process of obtaining the propulsion speed instability includes: In each image block of the highway subgrade surface edge difference image at each sampling time, an image coordinate system is constructed with the centroid point of the image block as the coordinate origin, and the vector corresponding to each edge point pointed to by the coordinate origin is used as the edge vector of each edge point in the image block; the edge vectors of all edge points in the image block are added to determine the edge distance vector of the corresponding image block; the modulus of the edge distance vector of the image block is positively correlated to determine the deformation centripetal density of each image block at each sampling time; The deformation centripetal density variance of each image block at all sampling moments before the current sampling moment is calculated; the advancement speed instability corresponding to the road compaction detection at the current moment is determined according to the mean of the deformation centripetal density variance corresponding to all image blocks.

4. A road compaction detection device according to claim 1, characterized in that: The process of obtaining the highway subgrade optimization adjustment coefficient includes: The product of the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed is normalized to determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment.

5. A road compaction detection device according to claim 1, characterized in that: The process of acquiring the adaptive propulsion speed includes: The negative correlation mapping value of the highway soil foundation optimization adjustment coefficient is used as a weight to weight the advancing speed at the current sampling moment to determine the adaptive advancing speed at the current sampling moment.

6. A road compaction detection device according to claim 1, characterized in that: The highway compaction detection after the highway soil foundation is advanced according to the adaptive advancement speed at the current sampling time includes: Continue to advance downward at an adaptive advancing speed until the ring cutter and digging teeth of the highway compaction detection equipment reach the preset advancing depth and then conduct highway compaction detection.

7. A road compaction detection device, comprising a ring cutter and a cylinder, characterized in that: The highway compaction detection equipment also includes a digging tooth advancing slide rail 1, a digging tooth advancing slide rail 2, a digging tooth 1, a digging tooth 2 and a highway soil foundation advancing monitoring equipment, the digging tooth advancing slide rail 1 and the digging tooth advancing slide rail 2 are respectively installed on both sides of the cylinder, the digging tooth 1 is fixedly installed inside the digging tooth advancing slide rail 1, and the digging tooth 2 is fixedly installed inside the digging tooth advancing slide rail 2, the highway soil foundation advancing monitoring equipment includes an image collector 1, an image collector 2 and a controller, the image collector 1 and the image collector 2 are both installed on the outside of the cylinder, the controller is installed inside the cylinder, the image collector 1 and the image collector 2 are both connected to the controller by signal, the image collector 1 and the image collector 2 are used to obtain highway soil foundation surface images at least two different perspectives, and the controller obtains the highway soil foundation surface edge image at each sampling moment according to all highway soil foundation surface images at each sampling moment; Determine the highway soil subgrade surface edge difference image at each sampling time according to the edge distribution difference of the highway soil subgrade surface edge image between adjacent sampling times; divide each highway soil subgrade surface edge image into at least two image blocks in the same division method; Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the distribution of edge lines of each image block in the highway subgrade surface edge difference image at each sampling moment before the current sampling moment; According to the fluctuation of the position of edge pixels in each image block in the edge difference image of the highway subgrade surface at each sampling time before the current sampling time, the instability of the advancement speed corresponding to the highway compaction detection at the current sampling time is determined; According to the initial adjustment coefficient of the highway soil foundation and the instability of the advancing speed, determine the highway soil foundation optimization adjustment coefficient corresponding to the highway compaction detection at the current sampling moment; according to the highway soil foundation optimization adjustment coefficient, adjust the advancing speed at the current sampling moment to obtain the adaptive advancing speed at the current sampling moment; After the highway soil foundation is advanced according to the adaptive advancement speed at the current sampling time, the highway compaction degree is tested; The process of obtaining the initial adjustment coefficient of the highway subgrade includes: According to the relative length of the longest edge line of each image block in the highway subgrade surface edge difference image at each sampling moment, the corresponding local deformation degree is determined; Determine the deformation sensitivity of each image block in the highway subgrade surface edge difference image at each sampling moment according to the average value of the local deformation degree of each image block in all highway subgrade surface edge images before each sampling moment; Determine the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment; The process of obtaining the local deformation degree includes: Each image block in the highway subgrade surface edge difference image at each sampling moment is sequentially used as a target image block; When there is no edge line other than a single edge point in the target image block, the local deformation degree of the target image block is set to a preset deformation degree value; When there are edge lines other than single edge points in the target image block, the average of the lengths of all edge lines corresponding to the non-single edge points in the target image block is used as the reference edge line length; the local deformation degree of the target image block is determined according to the ratio between the longest edge line length in the target image block and the reference edge line length; The method of determining the initial adjustment coefficient of the highway subgrade corresponding to the highway compaction detection at the current sampling moment according to the overall size of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment comprises: The normalized value of the accumulated values ​​of the deformation sensitivity of all image blocks in the highway subgrade surface edge difference image at the current sampling moment is used as the corresponding highway subgrade initial adjustment coefficient.

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